WO2017173476A1 - Application of data structures to geo-fencing applications - Google Patents
Application of data structures to geo-fencing applications Download PDFInfo
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- WO2017173476A1 WO2017173476A1 PCT/AU2017/000065 AU2017000065W WO2017173476A1 WO 2017173476 A1 WO2017173476 A1 WO 2017173476A1 AU 2017000065 W AU2017000065 W AU 2017000065W WO 2017173476 A1 WO2017173476 A1 WO 2017173476A1
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/02—Services making use of location information
- H04W4/029—Location-based management or tracking services
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S1/00—Beacons or beacon systems transmitting signals having a characteristic or characteristics capable of being detected by non-directional receivers and defining directions, positions, or position lines fixed relatively to the beacon transmitters; Receivers co-operating therewith
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/29—Geographical information databases
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/02—Services making use of location information
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/02—Services making use of location information
- H04W4/021—Services related to particular areas, e.g. point of interest [POI] services, venue services or geofences
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W52/00—Power management, e.g. TPC [Transmission Power Control], power saving or power classes
- H04W52/02—Power saving arrangements
- H04W52/0209—Power saving arrangements in terminal devices
- H04W52/0251—Power saving arrangements in terminal devices using monitoring of local events, e.g. events related to user activity
- H04W52/0254—Power saving arrangements in terminal devices using monitoring of local events, e.g. events related to user activity detecting a user operation or a tactile contact or a motion of the device
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W64/00—Locating users or terminals or network equipment for network management purposes, e.g. mobility management
- H04W64/003—Locating users or terminals or network equipment for network management purposes, e.g. mobility management locating network equipment
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D30/00—Reducing energy consumption in communication networks
- Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks
Definitions
- the field of the disclosure in this document is geo-fencing in particular ways to calculate the relative distance of a location from a geo-fence and associated central processing unit cycles involved in such calculations.
- Geo-fencing applications are required to use whatever computational power at its disposal to determine the position within a common datum, e.g. typically a mobile computing device, such as a mobile phone.
- a mobile computing device such as a mobile phone.
- one or more location-dependent applications could be running all of which use the considerable computing power of the device to determine whether that device is at a predefined location, crossed a geo-line or is external or internal to a predetermined geo-fenced zone.
- the applications also need to report entrance, and exit events of the mobile device entering or exiting a geo-fenced area and these use similar computational power and are based on location determination as well.
- Mechanisms of establishing the location of such a device commonly include cell tower (phone system) triangulation, Wi-Fi triangulation, applications of satellite-based navigation such as GPS, inertial navigation and Low Energy Bluetooth (BLE) signal based trilateration.
- cell tower phone system
- Wi-Fi wireless fidelity
- BLE Low Energy Bluetooth
- Geo-fencing applications increase the level of (typically) battery power used while establishing their location using one or more of the above mechanisms, with each mechanism having a different battery drain characteristic and accuracy of location determination, where generally the greater the accuracy, the greater the battery power usage (drain) and different applications using one or more of the mechanisms for establishing location at an appropriate time to suit the needs of the particular application.
- the curvature of the earth is not spherical but ellipsoidal (since the volume of the Earth is oblate, not spherical) the complexity of the calculation increases for long distances and each calculation is done with a selected datum. If the elevation is required, then a geoid model can be used so that some calculations will require an elevation component. Thus when location determination by the device is infrequent or when the geo-fenced area is a simple shape, the number and complexity of calculations performed is modest compared to frequent determinations and more complex geo-fence shapes regardless of the distances involved.
- Another way of determining distance would be to choose to calculate the distance to all of the vertices of the geo-fenced area, which are readily available, since they can be used to define the geo-fenced area very easily, and assumes that the line segments of the rectangle or square are derived from them anyway.
- a common practice is to create a minimum bounding frame, typically a square or rectangular shape, which generally has 4 sides, that touches the polygon at a minimum of four locations, effectively replacing the polygon with a rectangular shape that is as small as possible but still contains within it, all geo-locations that comprise the polygon.
- a minimum bounding frame typically a square or rectangular shape, which generally has 4 sides, that touches the polygon at a minimum of four locations, effectively replacing the polygon with a rectangular shape that is as small as possible but still contains within it, all geo-locations that comprise the polygon.
- Such a shape as illustrated in Figure 3, then simplifies the relative location calculations greatly by removing the first element in the equation - the number of addressable edges for each geo-fence from, in one example from 2000 to 4.
- the mobile device will recognize that it has entered the bounding frame (and may be required to perform particular actions when it has entered the polygonal geo-fence it represents), however due to its distance from the actual polygonal geo-fenced area, the action initiated may be unwanted, unnecessary, or in some cases potentially dangerous and considered a false trigger. Therefore, it is not practical to use a bounding frame approach for all polygonal geo- fences.
- a method to reduce central processor unit cycles of mobile computer device by minimising the number of positioning calculations that the mobile computer device needs to perform in reference to any number of complex polygonal geo-fences at the point of each location update, the method comprising the steps of: applying data structures to a deconstructed geo-fence prior to distance calculations for use in a geo-fencing application, wherein an existing geo-fence, represented as geospatial data, to decomposes the geo-fence into a multiplicity of smaller geo- fences which each represent a portion of the existing geo-fence until the multiplicity of smaller geo- fences are representative of the existing geo-fence; and determining the position of the mobile computing device with reference to the closest smaller geo-fence.
- step a) where each smaller geo-fence is deconstructed into a further number of yet smaller geo-fences when one of the smaller geo-fences intersects the boundary of the complex polygonal geofence this step being repeated for each of the smaller geo-fences to a level of granularity that is sufficient to replace the complex polygonal geofence with a large number of smaller geo-fences whose combined shape mirrors the complex polygonal geo-fence.
- spatial indexing is used to identify one or more of the smaller geo-fences of the existing geo-fence.
- the form of spatial indexing used is Quadtree data structure analysis to identify one or more of the smaller geo-fences of the existing geo-fence.
- the stored geospatial data is representative of selected complex polygonal geo-fences and those that are decomposed are only those that are in the vicinity of the mobile device.
- the predetermined data representative of the one or more geospatial data is required to represent the chosen geo-fence as a multitude of smaller geo-fences with a simpler geometry that when grouped, closely mirror the shape of the complex polygonal geo- fence and then applying the principles of Quadtree data structures on the subset of geo-fences until only the closest smaller geo-fence is located and using that smaller geo-fence for a positioning calculation.
- Figure 1 depicts a spherical earth representation having a point to point distance displayed
- Figure 2 depicts a sample set of formulae for calculating distance between two points on a sphere for small angles
- Figure 3 depicts a prior art approach to simplifying the determination of distance of a mobile device from a polygonal geo-fence by surrounding the polygon with a minimum bounding box and using the box for distance calculations instead of the polygon;
- Figure 4 depicts a complex polygonal geo-fence bounded by a prior art rectangular bounding frame over a geographic area showing road systems and geographic characteristics
- Figure 5 depicts a complex polygonal geo-fence the same as Figure 4 and a mobile computer device location X and illustrative of the calculations required to determine the distance of the device location from each side of the polygon;
- Figure 6 depicts a more complex polygonal shape than depicted in Figures 4 and 5 partially overlayed with smaller rectangular shapes generated by a Quadtree approach;
- Figure 7 depicts the complex polygonal shape of Figure 6 further partially overlayed with smaller rectangular shapes further generated by the Quadtree approach;
- Figure 8 depicts the complex polygonal shape of Figure 6 yet further partially overlayed with smaller rectangular shapes further generated by the Quadtree Approach
- Figure 9 depicts a flow diagram of the process of decomposing a geo-fence using Quadtree analysis
- Figure 10 depicts a small portion of the complex polygonal shape of Figure 6 illustrating the granularity once the required or predetermined minimum smaller geo-fence size is reached;
- Figure 1 1 depicts a spatial key indexing to geo-hash conversion example
- Figure 12 depicts an example of the flow of steps relating to distance determination of a mobile computer device from a pre-resolved granular geo-fence region which lies on the boundary of a complex polygonal geo-fence;
- Figure 13 depicts a representative of the use of geo-hashes to identify specific regions on a map with a particular representation
- Figure 14 an alternative representation of the creation of granular geo-fences to define a region (dark inner area) overlayed by parallelograms of the same shape formed in a grid pattern.
- Figure 5 is illustrative of the calculations, which are made from the location marked by the X, to each side of a complex polygon there being 7 arrows each representing a calculation of the distance of the nominal device position, marked with an X from a respective side of the polygon.
- the number of sides of the illustrated polygon is small relative to the possibilities (as shown by way of comparison with the complex polygonal shape depicted in Figure 6).
- a 2000 edged complex polygon (not shown) will still require the 2000 calculations.
- an existing complex polygonal geo-fence is deconstructed into a large number of smaller, simpler geo-fence shapes, which when treated as a consolidated shape collectively overlays the shape of the complex polygon.
- the first step is based on a starting boundary being the bounding box (the most Westerly, Northerly, Easterly and Southerly co-ordinates of the geo-fence can be used to create a virtual box) thus the entire complex polygonal geo-fence wholly lies within a very large shape referred to as the bounding box.
- the bounding box the most Westerly, Northerly, Easterly and Southerly co-ordinates of the geo-fence can be used to create a virtual box
- the bounding box the most Westerly, Northerly, Easterly and Southerly co-ordinates of the geo-fence can be used to create a virtual box
- Figures 6, 7 and 8 are illustrative of the decomposition (at least in part to illustrate the process) of the geo-fence boundary being bounded and then decomposed into multiple smaller regular polygons. The process is conducted multiple times to create a grid of data which represents multiple regular polygons (co-ordinates of which are part of the data) which each overlie the original complex polygon (represented best by a table of relevant data).
- each quadrant can be identified by a digital code.
- the terms top, bottom, left-hand and right-hand will be used, but they could also be substituted by the terms, North, South, West and East since this is geographically based environment and the latitude and longitude pairs (with horizontal values where applicable).
- the top left-hand quadrant is 00
- the top right-hand quadrant is 10
- the bottom left-hand quadrant is 01
- the bottom right-hand quadrant is 1 1.
- the bounding box has no size limitation and could traverse the Earth but is unlikely to be the case, however, the digital identification and transformation of those
- each quadrant is merely a range of latitude and longitude and since the predetermined sides of the complex polygon are defined by latitude longitudinal pairs, a comparison of respective values will soon determine whether a side of the complex polygon will fall within or outside a quadrant.
- that quadrant is quartered again.
- the bottom right-hand quadrant 11 is shown as four quadrants, where in accord with the digital identification, the top left-hand quadrant is 1100, the top right-hand quadrant is 1110, the bottom left-hand quadrant is 1101, and the bottom right-hand quadrant is 1111.
- each of them is quartered, but for illustration, the 1111 quadrant is shown in Figure 6 as being further quartered, into 111100, 111110, 111101 and 111111.
- a formed quadrant (111101) is intersected by any one or more lines of the polygon that quadrant is quartered again to become 11111000, 11111010, 1111101 and 11111011 as depicted in Figure 7. Where again it can be seen and accordingly determined that 11111010 be ignored (OUT).
- Figure 9 depicts a flow diagram of the repeated process of decomposing a geo-fence using Quadtree analysis and testing to determine whether the segmentation should continue or not.
- Figure 10 is a representation of the selected granularity of the final quartering, such that the small rectangular areas showed in the Figure, all include a portion of the complex polygonal shape and as such those small rectangular areas are representative of that complex polygonal shape (thus mirroring that shape to any required level of granularity). Since each of those areas have a digital identity then it becomes possible to sort, re-arrange and in particular analyse those digital identities using computational methods which are very CPU efficient.
- a polygonal geo-fence (with e.g.2000 edges) is defined by recording the coordinates of each point where two adjacent edges meet (ignoring self-intersecting edges).
- the polygonal geo-offence is surrounded by a minimum bounding box, and the bounding box is split into equal quarters. A query is run to determine if any of the quarters contains any portion of the original polygon.
- the quarter as being wholly contained within the polygon and record a rectangular (including a square) geo-fence that is to be used as a substituting element of the original polygon.
- the wholly contained geo-fence is associated with a geo-hash that indicates its position within the bounding box and within a common geospatial datum.
- a geo-hash is a geocoding system comprised of a hierarchical spatial data structure which divides space (such as the surface of the earth) into grids, and the format of the reference for each grid is indicative of each grids relationship with other grid references, such as short Uniform Resource Locators (URLs) which uniquely identify positions on the Earth, so that referencing them in emails, forums, and websites and also in tables and databases, is more convenient.
- the e structure of geo-hashed data has two advantages. First, data indexed by geo-hash will have all points for a given rectangular area in contiguous slices (the number of slices depends on the precision required and the presence of geo-hash "fault lines"). This is especially useful in database systems where queries on a single index are much easier or faster than multiple-index queries.
- this index structure can be used for a quick-and-dirty proximity search: the closest points are often among the closest geo-hashes. More about the format and use of geo-hashes is provided later in the specification.
- the rectangular geo-fence is stored within the device's memory along with the geo-hash. All overlapping constituting geo-fences are combined in the device's memory to form a new representation of the polygonal geo-fence with each element retaining its individual geo-hash.
- the quadrant there is, of course, a limit to how small the quadrant can or needs to become.
- the way in which the limit is predetermined is determined before the beginning of the process, or on the fly, and can be dependent on many factors.
- the land areas of the first bounding box are determined, and if large, the size (area) of the smallest quadrant is large, compared to a smaller land area of the first bounding box, using smaller
- the limit on the minimum quadrant size can be determined by exceeding the resolution of the GPS chipset used by the respective device, at least by several factors, i.e. if a segmented quadrant is 10cm wide, and the best GPS accuracy is e.g. 5 meters, then there is no need to segment further.
- the shape generated by this analysis consists of many quadrants (possibly many thousands), but it will not be the exact shape of the polygon, but it will be very close (dependant on the maximum resolution or number of quadrants generated).
- Figure 10 is an exploded view of a very small portion of the image of the complex polygon of Figures 6, 7 and 8 taken to the predetermined level of quartering.
- the length of the digital identification of the rectangular geo-fence is likely to be very long, e.g.
- a complex polygonal geo-fence with e.g. 2000 edges and in an aspect it is disclosed that a complex polygonal geo-fence can be represented by a greater multitude (possibly tens of thousands) of regular shaped geo-fences there are then many times 2000 more edges to deal with and thus a need for the device to perform a significant number of calculations being many more than the 2000 edges approach described previously.
- Quadtree analysis is the most used recursive decomposition method and therefore included as an embodiment, but that does not exclude other analysis techniques.
- the Quadtree approach provides a balanced tree structure of degree four. This hierarchical model where each node has four sons provides a distinct advantage to computational performance of the analysis because it exhibits a tree file structure which allows for compaction techniques and efficient file addressing schemes.
- the quadrant identification coding described by way of example illustrates this property.
- locating the geographically closest quadrant knowing the current location of a device can be illustrated by moving from the closest largest quadrant 1 1, to the second largest closest quadrant 1 1 1 1 and then eventually to 1 1 1 1011 1 1 1...1 1.
- a geo-hash is a representation of these long digital identifications which is explained later in this specification.
- Many chosen approaches can be used to ultimately reduce (compared with the described prior art method) the CPU cycles devoted to the initial analysis of large amounts of spatial data.
- recursive subdivision facilitates windowing of large spatial databases representative of, say multiple geo-fence zones, to relatively quickly identify those geo-fences that do not need to be analysed.
- the Quadtree approach uses a general data model having a hierarchical data structure based on the principle of recursive decomposition using quadrants.
- An example of a data set representing the many regular shaped geo-fences is provided in Figure 1 1, and in this example, the digital identification used is different to that used previously, but that does not change the approach or the methods used.
- Quadtree data structures are applied to the new set of regular geo- fences to locate the closest smaller geo-fence which is representative of a portion of the entire complex polygon.
- a mobile device When applying the principles of Quadtree data structures, a mobile device is capable of storing, in a suitable format, the multitude of smaller geo-fences so that the process of Quadtree analysis can be most efficiently applied, to thus minimise the CPU cycles involved in the analysis of the stored data.
- the CPU of the mobile device is to determine which quadrant or grouping of the geo-fences it is closest to, and thus being able to ignore the other 3 quadrants.
- Figure 12 is a flow diagram of the process conducted by the mobile computer device.
- the mobile device receives a new location update in the form of co-ordinates pertaining to a common datum.
- the device will translate its location in a co-ordinate form to a geo-hash reference.
- the device will then analyse the stored representation of the surrounding polygonal geo- fences consisting of a multitude of rectangular geo-fences.
- the device determines through geo-hash analysis which individual rectangular geo-fence is closest to the current location.
- the device and then measures its distance to the closest rectangular geo-fence. [0091 ] Details regarding the determination of closeness can be dealt with in many ways without having to perform distance calculations, and by way of example only, geo-hash tables and sorting techniques can be used, depending on the format of the geospatial data.
- Quadtree-like data structure for storing polygonal geo-fence representations using, for example, a PR Quadtree and PM Quadtree some with more or less ability to store points, lines and regions depending on the granularity.
- the complex polygonal geo-fence data processing done to determine the relevant data is CPU intensive, but need only occur when needed at the time of downloading the geo-fence data.
- the mobile computer device say a mobile phone device
- receives or generates with an on-board geolocation determination device as a location update the mobile computer device need only determine which of the already granular (typically very small) rectangular or square geo-fence region is closest based on the geo-hash, and then the mobile computer device determines the distance to only that one small geo-fence, disregarding others.
- the computing power required is used for each of the potentially 1,000's of separate geo- fence zones that can be used by one or more applications on the mobile computing device, but, only once each time it is down loaded not for each and every geo-fence, each and every time since the nearest geo-hash associated with a granular geo-fence is a traversal problem, not a multiple distance calculation.
- the closest granular geo-fence is analysed by performing multiple rounds of Quadtree deconstruction on the grid/table representative of that geo-fence, until the device needs to take into account only a single smaller geo-fence (from the subset of geo-fences extracted from the larger polygonal geo-fence) but which still lies on the geo-fence boundary.
- This particular smaller geo-fence is determined to be closest to the mobile device at the time of the deconstruction, and thus usable to determine the distance of the mobile computer device from that single smaller geo-fence.
- the CPU usage in performing hierarchical analysis of the suitably formatted data is much more efficient than any prior approach.
- a simple example uses a 3 character string containing only numbers. (that can only be 0,1,2,3 as per Quadtree principles - there can only be one of 4 character values in each position, i.e.: 000, 001, 002, 003
- a device determines that it has entered the smaller geo-fence (which lies on, the larger complex polygonal geo-fence boundary) or at or within a predetermined proximity to the same smaller geo-fence, the application will deal with the notification in accordance with the requirement of that application.
- there will an associated action such as for example, provision of a notice to the user of the mobile device that they have entered a zone of interest to the user of the mobile device.
- the mode in which the device stores geospatial reference data such as geo-fences, geo- spatial co-ordinates, lines defined by any means and the determined position of the mobile device, as well as the datum the device utilises should preferably be in a format that enables appropriate analysis using the approaches available.
- map data structures of which there are many forms form the basis for the display and analysis of cartographic data.
- Some examples are grid structure, Quadtree and tessellations plus device specific entity-by-entity structures all designed for use by computer devices.
- that information can be organized in a way to enable that data to be read to and be physically installed on to a storage medium, in a method of representation that allows a computer device to symbolize cartographic objects on maps and in the environment of the disclosure; the location of the mobile computer device; and geo-fence data with relative ease and within a reasonable length of time.
- the device will have to work through the Quadtree data to sort out which data is relevant and which is not, however, the sorting process only involves a few steps and with correct data indexing procedures, the data sorting process is exponentially more power and CPU cycle use efficient compared to high numbers of positioning calculations.
- Spatial indexing could involve the use of geo-hashes, and the manner of determining the closeness of a geographic location can be dealt with using a 1 D comparison of the geo-hash since a common prefix indicates a common source and the number of common prefixes is indicative of the accuracy.
- r1f93ch75x8y is close to r1f93ch75x8 as is r1f93ch75x but the area it represents only means that r1f93ch75x8y is within r1f93ch75x.
- ryf93dh35xya is far away and not relevant.
- Figure 13 is roughly representative of the use of geo-hashes to identify specific regions on a map with a particular representation, for example, dr5ruu2 is near dr5uu1 and dr5uu3, and all are within dr5uu, and a location (dot) is located in a respective region.
- Figure 14 depicts an alternative representation of the creation of granular geo-fences to define a region (dark inner area) overlayed by parallelograms of the same shape formed in a grid pattern. This illustrates that the granular geo-fenced areas that could be used to represent the complex larger geo-fence can be other than rectangles and squares.
- the last known location of the device provides a handy base from which to start, ensuring that an app has a known location before starting any periodic location updates.
- Getting the Last Known Location is a step that is achieved by calling getLastLocation().
- the code can be created assuming that the app has already retrieved the last known location and stored it as a Location object in a global variable mCurrentLocation.
- Apps that use location services must request location permissions. Typically they require fine location detection so that the app can get as precise a location as possible from the available location provider device/s. Requesting this permission with the uses-permission element in the app manifest, as is shown in the following example:
- LocationListener.onLocationChanged() callback method and passes it a Location object, or issues a Pendinglntent that contains the location in its extended data.
- the accuracy and frequency of the updates are affected by the location permissions requested and the options set in the location request object.
Abstract
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CA3015384A CA3015384A1 (en) | 2016-04-07 | 2017-03-16 | Application of data structures to geo-fencing applications |
AU2017245932A AU2017245932A1 (en) | 2016-04-07 | 2017-03-16 | Application of data structures to geo-fencing applications |
EP17778460.0A EP3424244A4 (en) | 2016-04-07 | 2017-03-16 | Application of data structures to geo-fencing applications |
US16/078,619 US11082802B2 (en) | 2016-04-07 | 2017-03-16 | Application of data structures to geo-fencing applications |
AU2020203554A AU2020203554B2 (en) | 2016-04-07 | 2020-05-29 | Application of data structures to geo-fencing applications |
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Also Published As
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US20190045324A1 (en) | 2019-02-07 |
AU2020203554B2 (en) | 2021-05-13 |
AU2020203554A1 (en) | 2020-06-18 |
EP3424244A4 (en) | 2019-09-25 |
CA3015384A1 (en) | 2017-10-12 |
US11082802B2 (en) | 2021-08-03 |
EP3424244A1 (en) | 2019-01-09 |
AU2017245932A1 (en) | 2018-07-19 |
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